Toward Evolving Dispatching Rules for Dynamic Job Shop Scheduling Under Uncertainty
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Karunakaran:2017:GECCO,
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author = "Deepak Karunakaran and Yi Mei and Gang Chen2 and
Mengjie Zhang",
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title = "Toward Evolving Dispatching Rules for Dynamic Job Shop
Scheduling Under Uncertainty",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4920-8",
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address = "Berlin, Germany",
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pages = "282--289",
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size = "8 pages",
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URL = "http://doi.acm.org/10.1145/3071178.3071202",
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DOI = "doi:10.1145/3071178.3071202",
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acmid = "3071202",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, job shop
scheduling, uncertainty",
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month = "15-19 " # jul,
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abstract = "Dynamic job shop scheduling (DJSS) is a complex
problem which is an important aspect of manufacturing
systems. Even though the manufacturing environment is
uncertain, most of the existing research works consider
deterministic scheduling problems where the time
required for processing any job is known in advance and
never changes. In this work, we consider DJSS problems
with varied uncertainty configurations of machines in
terms of processing times and the total flow time as
scheduling objective. With the varying levels of
uncertainty many machines become bottlenecks of the job
shop. It is essential to identify these bottleneck
machines and schedule the jobs to be performed by them
carefully. Driven by this idea, we develop a new
effective method to evolve pairs of dispatching rules
each for a different bottleneck level of the machines.
A clustering approach to classifying the bottleneck
level of the machines arising in the system due to
uncertain processing times is proposed. Then, a
cooperative co-evolution technique to evolve pairs of
dispatching rules which generalize well across
different uncertainty configurations is presented. We
perform empirical analysis to show its generalization
characteristic over the different uncertainty
configurations and show that the proposed method
outperforms the current approaches.",
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notes = "Also known as
\cite{Karunakaran:2017:TED:3071178.3071202} GECCO-2017
A Recombination of the 26th International Conference on
Genetic Algorithms (ICGA-2017) and the 22nd Annual
Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Deepak Karunakaran
Yi Mei
Aaron Chen
Mengjie Zhang
Citations